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701 lines
20 KiB
Python
701 lines
20 KiB
Python
from __future__ import annotations
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import logging
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from collections import OrderedDict, defaultdict
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from collections.abc import MutableMapping, Mapping, Set
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from abc import ABCMeta
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from copy import deepcopy, copy
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from functools import partial, wraps
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from itertools import islice, chain
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import inspect
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import types
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import textwrap
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import networkx as nx
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import warnings
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import sys
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from typing import Any
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from mesa import Agent as MesaAgent, Model
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from typing import Dict, List
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from .. import serialization, network, utils, time, config
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IGNORED_FIELDS = ("model", "logger")
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def decorate_generator_step(func, name):
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@wraps(func)
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def decorated(self):
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while True:
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if self._coroutine is None:
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self._coroutine = func(self)
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try:
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if self._last_except:
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val = self._coroutine.throw(self._last_except)
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else:
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val = self._coroutine.send(self._last_return)
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except StopIteration as ex:
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self._coroutine = None
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val = ex.value
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finally:
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self._last_return = None
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self._last_except = None
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return float(val) if val is not None else val
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return decorated
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def decorate_normal_func(func, name):
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@wraps(func)
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def decorated(self):
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val = func(self)
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return float(val) if val is not None else val
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return decorated
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class MetaAgent(ABCMeta):
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def __new__(mcls, name, bases, namespace):
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defaults = {}
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# Re-use defaults from inherited classes
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for i in bases:
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if isinstance(i, MetaAgent):
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defaults.update(i._defaults)
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new_nmspc = {
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"_defaults": defaults,
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}
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for attr, func in namespace.items():
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if attr == "step":
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if inspect.isgeneratorfunction(func) or inspect.iscoroutinefunction(func):
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func = decorate_generator_step(func, attr)
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new_nmspc.update({
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"_last_return": None,
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"_last_except": None,
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"_coroutine": None,
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})
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elif inspect.isasyncgenfunction(func):
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raise ValueError("Illegal step function: {}. It probably mixes both async/await and yield".format(func))
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elif inspect.isfunction(func):
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func = decorate_normal_func(func, attr)
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else:
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raise ValueError("Illegal step function: {}".format(func))
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new_nmspc[attr] = func
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elif (
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isinstance(func, types.FunctionType)
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or isinstance(func, property)
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or isinstance(func, classmethod)
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or attr[0] == "_"
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):
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new_nmspc[attr] = func
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elif attr == "defaults":
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defaults.update(func)
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elif inspect.isfunction(func):
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new_nmspc[attr] = func
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else:
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defaults[attr] = copy(func)
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# Add attributes for their use in the decorated functions
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return super().__new__(mcls, name, bases, new_nmspc)
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class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
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"""
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A special type of Mesa Agent that:
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* Can be used as a dictionary to access its state.
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* Has logging built-in
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* Can be given default arguments through a defaults class attribute,
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which will be used on construction to initialize each agent's state
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Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
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"""
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def __init__(self, unique_id, model, name=None, init=True, **kwargs):
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assert isinstance(unique_id, int)
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super().__init__(unique_id=unique_id, model=model)
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self.name = (
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str(name) if name else "{}[{}]".format(type(self).__name__, self.unique_id)
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)
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self.alive = True
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logger = utils.logger.getChild(getattr(self.model, "id", self.model)).getChild(
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self.name
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)
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self.logger = logging.LoggerAdapter(logger, {"agent_name": self.name})
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if hasattr(self, "level"):
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self.logger.setLevel(self.level)
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for k in self._defaults:
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v = getattr(model, k, None)
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if v is not None:
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setattr(self, k, v)
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for (k, v) in self._defaults.items():
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if not hasattr(self, k) or getattr(self, k) is None:
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setattr(self, k, deepcopy(v))
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for (k, v) in kwargs.items():
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setattr(self, k, v)
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if init:
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self.init()
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def init(self):
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pass
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def __hash__(self):
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return hash(self.unique_id)
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def prob(self, probability):
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return prob(probability, self.model.random)
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@classmethod
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def w(cls, **kwargs):
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return custom(cls, **kwargs)
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# TODO: refactor to clean up mesa compatibility
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@property
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def id(self):
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msg = "This attribute is deprecated. Use `unique_id` instead"
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warnings.warn(msg, DeprecationWarning)
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print(msg, file=sys.stderr)
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return self.unique_id
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@classmethod
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def from_dict(cls, model, attrs, warn_extra=True):
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ignored = {}
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args = {}
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for k, v in attrs.items():
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if k in inspect.signature(cls).parameters:
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args[k] = v
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else:
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ignored[k] = v
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if ignored and warn_extra:
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utils.logger.info(
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f"Ignoring the following arguments for agent class { agent_class.__name__ }: { ignored }"
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)
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return cls(model=model, **args)
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def __getitem__(self, key):
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try:
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return getattr(self, key)
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except AttributeError:
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raise KeyError(f"key {key} not found in agent")
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def __delitem__(self, key):
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return delattr(self, key)
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def __contains__(self, key):
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return hasattr(self, key)
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def __setitem__(self, key, value):
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setattr(self, key, value)
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def __len__(self):
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return sum(1 for n in self.keys())
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def __iter__(self):
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return self.items()
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def keys(self):
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return (k for k in self.__dict__ if k[0] != "_" and k not in IGNORED_FIELDS)
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def items(self, keys=None, skip=None):
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keys = keys if keys is not None else self.keys()
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it = ((k, self.get(k, None)) for k in keys)
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if skip:
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return filter(lambda x: x[0] not in skip, it)
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return it
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def get(self, key, default=None):
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try:
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return getattr(self, key)
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except AttributeError:
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try:
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return getattr(self.model, key)
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except AttributeError:
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return default
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@property
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def now(self):
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try:
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return self.model.now
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except AttributeError:
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# No environment
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return None
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def die(self, msg=None):
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if msg:
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self.info("Agent dying:", msg)
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else:
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self.debug(f"agent dying")
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self.alive = False
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try:
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self.model.schedule.remove(self)
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except KeyError:
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pass
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return time.Delay(time.INFINITY)
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def step(self):
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raise NotImplementedError("Agent must implement step method")
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def _check_alive(self):
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if not self.alive:
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raise time.DeadAgent(self.unique_id)
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def log(self, *message, level=logging.INFO, **kwargs):
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if not self.logger.isEnabledFor(level):
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return
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message = " ".join(str(i) for i in message)
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message = "[@{:>4}]\t{:>10}: {}".format(self.now, repr(self), message)
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for k, v in kwargs:
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message += " {k}={v} ".format(k, v)
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extra = {}
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extra["now"] = self.now
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extra["unique_id"] = self.unique_id
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extra["agent_name"] = self.name
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return self.logger.log(level, message, extra=extra)
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def debug(self, *args, **kwargs):
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return self.log(*args, level=logging.DEBUG, **kwargs)
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def info(self, *args, **kwargs):
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return self.log(*args, level=logging.INFO, **kwargs)
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def count_agents(self, **kwargs):
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return len(list(self.get_agents(**kwargs)))
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def get_agents(self, *args, **kwargs):
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it = self.iter_agents(*args, **kwargs)
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return list(it)
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def iter_agents(self, *args, **kwargs):
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yield from filter_agents(self.model.schedule._agents, *args, **kwargs)
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def __str__(self):
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return self.to_str()
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def to_str(self, keys=None, skip=None, pretty=False):
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content = dict(self.items(keys=keys))
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if pretty and content:
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d = content
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content = "\n"
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for k, v in d.items():
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content += f"- {k}: {v}\n"
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content = textwrap.indent(content, " ")
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return f"{repr(self)}{content}"
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def __repr__(self):
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return f"{self.__class__.__name__}({self.unique_id})"
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def at(self, at):
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return time.Delay(float(at) - self.now)
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def delay(self, delay=1):
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return time.Delay(delay)
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def prob(prob, random):
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"""
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A true/False uniform distribution with a given probability.
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To be used like this:
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.. code-block:: python
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if prob(0.3):
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do_something()
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"""
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r = random.random()
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return r < prob
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def calculate_distribution(network_agents=None, agent_class=None):
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"""
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Calculate the threshold values (thresholds for a uniform distribution)
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of an agent distribution given the weights of each agent type.
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The input has this form: ::
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[
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{'agent_class': 'agent_class_1',
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'weight': 0.2,
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'state': {
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'id': 0
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}
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},
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{'agent_class': 'agent_class_2',
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'weight': 0.8,
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'state': {
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'id': 1
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}
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}
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]
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In this example, 20% of the nodes will be marked as type
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'agent_class_1'.
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"""
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if network_agents:
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network_agents = [
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deepcopy(agent) for agent in network_agents if not hasattr(agent, "id")
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]
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elif agent_class:
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network_agents = [{"agent_class": agent_class}]
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else:
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raise ValueError("Specify a distribution or a default agent type")
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# Fix missing weights and incompatible types
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for x in network_agents:
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x["weight"] = float(x.get("weight", 1))
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# Calculate the thresholds
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total = sum(x["weight"] for x in network_agents)
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acc = 0
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for v in network_agents:
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if "ids" in v:
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continue
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upper = acc + (v["weight"] / total)
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v["threshold"] = [acc, upper]
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acc = upper
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return network_agents
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def _serialize_type(agent_class, known_modules=[], **kwargs):
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if isinstance(agent_class, str):
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return agent_class
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known_modules += ["soil.agents"]
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return serialization.serialize(agent_class, known_modules=known_modules, **kwargs)[
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1
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] # Get the name of the class
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def _deserialize_type(agent_class, known_modules=[]):
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if not isinstance(agent_class, str):
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return agent_class
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known = known_modules + ["soil.agents", "soil.agents.custom"]
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agent_class = serialization.deserializer(agent_class, known_modules=known)
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return agent_class
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class AgentView(Mapping, Set):
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"""A lazy-loaded list of agents."""
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__slots__ = ("_agents",)
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def __init__(self, agents):
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self._agents = agents
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def __getstate__(self):
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return {"_agents": self._agents}
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def __setstate__(self, state):
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self._agents = state["_agents"]
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# Mapping methods
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def __len__(self):
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return len(self._agents)
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def __iter__(self):
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yield from self._agents.values()
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def __getitem__(self, agent_id):
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if isinstance(agent_id, slice):
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raise ValueError(f"Slicing is not supported")
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if agent_id in self._agents:
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return self._agents[agent_id]
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raise ValueError(f"Agent {agent_id} not found")
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def filter(self, *args, **kwargs):
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yield from filter_agents(self._agents, *args, **kwargs)
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def one(self, *args, **kwargs):
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return next(filter_agents(self._agents, *args, **kwargs))
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def __call__(self, *args, **kwargs):
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return list(self.filter(*args, **kwargs))
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def __contains__(self, agent_id):
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return agent_id in self._agents
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def __str__(self):
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return str(list(unique_id for unique_id in self.keys()))
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def __repr__(self):
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return f"{self.__class__.__name__}({self})"
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def filter_agents(
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agents: dict,
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*id_args,
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unique_id=None,
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state_id=None,
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agent_class=None,
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ignore=None,
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state=None,
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limit=None,
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**kwargs,
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):
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"""
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Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id).
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"""
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assert isinstance(agents, dict)
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ids = []
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if unique_id is not None:
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if isinstance(unique_id, list):
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ids += unique_id
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else:
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ids.append(unique_id)
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if id_args:
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ids += id_args
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if ids:
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f = (agents[aid] for aid in ids if aid in agents)
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else:
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f = agents.values()
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if state_id is not None and not isinstance(state_id, (tuple, list)):
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state_id = tuple([state_id])
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if agent_class is not None:
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agent_class = _deserialize_type(agent_class)
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try:
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agent_class = tuple(agent_class)
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except TypeError:
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agent_class = tuple([agent_class])
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if ignore:
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f = filter(lambda x: x not in ignore, f)
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if state_id is not None:
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f = filter(lambda agent: agent.get("state_id", None) in state_id, f)
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if agent_class is not None:
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f = filter(lambda agent: isinstance(agent, agent_class), f)
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state = state or dict()
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state.update(kwargs)
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for k, vs in state.items():
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if not isinstance(vs, list):
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vs = [vs]
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f = filter(lambda agent: any(getattr(agent, k, None) == v for v in vs), f)
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if limit is not None:
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f = islice(f, limit)
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yield from f
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def from_config(
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cfg: config.AgentConfig, random, topology: nx.Graph = None
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) -> List[Dict[str, Any]]:
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"""
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This function turns an agentconfig into a list of individual "agent specifications", which are just a dictionary
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with the parameters that the environment will use to construct each agent.
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This function does NOT return a list of agents, mostly because some attributes to the agent are not known at the
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time of calling this function, such as `unique_id`.
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"""
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default = cfg or config.AgentConfig()
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if not isinstance(cfg, config.AgentConfig):
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cfg = config.AgentConfig(**cfg)
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agents = []
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assigned_total = 0
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assigned_network = 0
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if cfg.fixed is not None:
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agents, assigned_total, assigned_network = _from_fixed(
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cfg.fixed, topology=cfg.topology, default=cfg
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)
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n = cfg.n
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if cfg.distribution:
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topo_size = len(topology) if topology else 0
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networked = []
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total = []
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for d in cfg.distribution:
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if d.strategy == config.Strategy.topology:
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topo = d.topology if ("topology" in d.__fields_set__) else cfg.topology
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if not topo:
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raise ValueError(
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'The "topology" strategy only works if the topology parameter is set to True'
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)
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if not topo_size:
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raise ValueError(
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f"Topology does not have enough free nodes to assign one to the agent"
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)
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networked.append(d)
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if d.strategy == config.Strategy.total:
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if not cfg.n:
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raise ValueError(
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'Cannot use the "total" strategy without providing the total number of agents'
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)
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total.append(d)
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if networked:
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new_agents = _from_distro(
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networked,
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n=topo_size - assigned_network,
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topology=topo,
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default=cfg,
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random=random,
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)
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assigned_total += len(new_agents)
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assigned_network += len(new_agents)
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agents += new_agents
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if total:
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remaining = n - assigned_total
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agents += _from_distro(total, n=remaining, default=cfg, random=random)
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if assigned_network < topo_size:
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utils.logger.warn(
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f"The total number of agents does not match the total number of nodes in "
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|
"every topology. This may be due to a definition error: assigned: "
|
|
f"{ assigned } total size: { topo_size }"
|
|
)
|
|
|
|
return agents
|
|
|
|
|
|
def _from_fixed(
|
|
lst: List[config.FixedAgentConfig],
|
|
topology: bool,
|
|
default: config.SingleAgentConfig,
|
|
) -> List[Dict[str, Any]]:
|
|
agents = []
|
|
|
|
counts_total = 0
|
|
counts_network = 0
|
|
|
|
for fixed in lst:
|
|
agent = {}
|
|
if default:
|
|
agent = default.state.copy()
|
|
agent.update(fixed.state)
|
|
cls = serialization.deserialize(
|
|
fixed.agent_class or (default and default.agent_class)
|
|
)
|
|
agent["agent_class"] = cls
|
|
topo = (
|
|
fixed.topology
|
|
if ("topology" in fixed.__fields_set__)
|
|
else topology or default.topology
|
|
)
|
|
|
|
if topo:
|
|
agent["topology"] = True
|
|
counts_network += 1
|
|
if not fixed.hidden:
|
|
counts_total += 1
|
|
agents.append(agent)
|
|
|
|
return agents, counts_total, counts_network
|
|
|
|
|
|
def _from_distro(
|
|
distro: List[config.AgentDistro],
|
|
n: int,
|
|
default: config.SingleAgentConfig,
|
|
random,
|
|
topology: str = None
|
|
) -> List[Dict[str, Any]]:
|
|
|
|
agents = []
|
|
|
|
if n is None:
|
|
if any(lambda dist: dist.n is None, distro):
|
|
raise ValueError(
|
|
"You must provide a total number of agents, or the number of each type"
|
|
)
|
|
n = sum(dist.n for dist in distro)
|
|
|
|
weights = list(dist.weight if dist.weight is not None else 1 for dist in distro)
|
|
minw = min(weights)
|
|
norm = list(weight / minw for weight in weights)
|
|
total = sum(norm)
|
|
chunk = n // total
|
|
|
|
# random.choices would be enough to get a weighted distribution. But it can vary a lot for smaller k
|
|
# So instead we calculate our own distribution to make sure the actual ratios are close to what we would expect
|
|
|
|
# Calculate how many times each has to appear
|
|
indices = list(
|
|
chain.from_iterable([idx] * int(n * chunk) for (idx, n) in enumerate(norm))
|
|
)
|
|
|
|
# Complete with random agents following the original weight distribution
|
|
if len(indices) < n:
|
|
indices += random.choices(
|
|
list(range(len(distro))),
|
|
weights=[d.weight for d in distro],
|
|
k=n - len(indices),
|
|
)
|
|
|
|
# Deserialize classes for efficiency
|
|
classes = list(
|
|
serialization.deserialize(i.agent_class or default.agent_class) for i in distro
|
|
)
|
|
|
|
# Add them in random order
|
|
random.shuffle(indices)
|
|
|
|
for idx in indices:
|
|
d = distro[idx]
|
|
agent = d.state.copy()
|
|
cls = classes[idx]
|
|
agent["agent_class"] = cls
|
|
if default:
|
|
agent.update(default.state)
|
|
topology = (
|
|
d.topology
|
|
if ("topology" in d.__fields_set__)
|
|
else topology or default.topology
|
|
)
|
|
if topology:
|
|
agent["topology"] = topology
|
|
agents.append(agent)
|
|
|
|
return agents
|
|
|
|
|
|
from .network_agents import *
|
|
from .fsm import *
|
|
from .evented import *
|
|
from typing import Optional
|
|
|
|
|
|
class Agent(NetworkAgent, FSM, EventedAgent):
|
|
"""Default agent class, has both network and event capabilities"""
|
|
|
|
|
|
from ..environment import NetworkEnvironment
|
|
|
|
|
|
from .BassModel import *
|
|
from .IndependentCascadeModel import *
|
|
from .SISaModel import *
|
|
from .CounterModel import *
|
|
|
|
|
|
def custom(cls, **kwargs):
|
|
"""Create a new class from a template class and keyword arguments"""
|
|
return type(cls.__name__, (cls,), kwargs)
|